doing 1000 permutations and doing test statistics distribution

Tue, Feb 4, 2020 12:46 PM

Hi Bert,

thanks for getting back to me. I have to permute those 132 columns
1000 times and perform the code given in the previous email.

Can you please show me how you would do that in the loop? This is also
a huge data set ...

Thanks
Ana

On Tue, Feb 4, 2020 at 2:34 PM Bert Gunter <bgunter.4567 at gmail.com> wrote:

If you just want to permute columns of a matrix,

?sample

sample.int(10)

 [1]  9  2 10  8  4  6  3  1  5  7

and you can just use this as an index into the columns of your matrix, presumably within a loop of some sort.

If I have misunderstood, just ignore.

Cheers,
Bert




On Tue, Feb 4, 2020 at 12:23 PM Ana Marija <sokovic.anamarija at gmail.com> wrote:

Hello,

I have a matrix

dim(dat)

[1] 15568   132

It looks like this:

                   NoD_14381_norm.1 NoD_14381_norm.2 NoD_14381_norm.3
NoD_14520_30mM.1 NoD_14520_30mM.2 NoD_14520_30mM.3
Ku8QhfS0n_hIOABXuE             4.75             4.25             4.79
           4.33             4.63             3.85
Bx496XsFXiAlj.Eaeo             6.15             6.23             6.55
           6.26             6.24             5.99
W38p0ogk.wIBVRXllY             7.13             7.35             7.55
           7.37             7.36             7.55
QIBkqIS9LR5DfTlTS8             6.27             6.73             6.45
           5.39             4.75             4.96
BZKiEvS0eQ305U0v34             6.35             7.02             6.76
           5.45             5.25             5.02
6TheVd.HiE1UF3lX6g             5.53             5.02             5.36
           5.61             5.66             5.37

So it is a matrix with gene names ex. Ku8QhfS0n_hIOABXuE, and subjects
named ex. NoD_14381_norm.1


How to do 1000 permutations of these 132 columns and on each created
new permuted matrix perform this code:

subject="all_replicate"
targets<-readTargets(paste(PhenotypeDir,"hg_sg_",subject,"_target.txt", sep=''))
Treat <- factor(targets$Treatment,levels=c("C","T"))
Replicates <- factor(targets$rep)
design <- model.matrix(~Replicates+Treat)
corfit <- duplicateCorrelation(dat, block = targets$Subject)
corfit$consensus.correlation
fit <-lmFit(dat,design,block=targets$Subject,correlation=corfit$consensus.correlation)
fit<-eBayes(fit)
qval.cutoff=0.1; FC.cutoff=0.17
y1=topTable(fit, coef="TreatT", n=nrow(genes),adjust.method="BH",genelist=genes)

y1 for each iteration of permutation would  have P.Value column and
these I would have plotted on the end to find the distribution of all
p values generated in those 1000 permutations.

Please advise,
Ana

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doing 1000 permutations and doing test statistics distribution

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